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An improvement to the existing technique of quantifying signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images using piecewise cubic Hermite interpolation (PCHIP) technique is proposed. The new technique uses an adaptive tuning onto the PCHIP, and is thus named as ATPCHIP. To test its accuracy, 70 images are corrupted with noise and their autocorrelation functions are then plotted. The ATPCHIP technique is applied to estimate the uncorrupted noise-free zero offset point from a corrupted image. Three existing methods, the nearest neighborhood, first order interpolation and original PCHIP, are used to compare with the performance of the proposed ATPCHIP method, with respect to their calculated SNR values. Results show that ATPCHIP is an accurate and reliable method to estimate SNR values from SEM images. SCANNING 38:502-514, 2016. © 2015 Wiley Periodicals, Inc.

Image processing is introduced to remove or reduce the noise and unwanted signal that deteriorate the quality of an image. Here, a single level two‐dimensional wavelet transform is applied to the image in order to obtain the wavelet transform sub‐band signal of an image. An estimation technique to predict the noise variance in an image is proposed, which is then fed into a Wiener filter to filter away the noise from the sub‐band of the image. The proposed filter is called adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in the wavelet domain. The performance of this filter is compared with four existing filters: median filter, Gaussian smoothing filter, two level wavelet transform with Wiener filter and adaptive noise Wiener filter. Based on the results, the adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain has better performance than the other four methods.

A new technique to quantify signal-to-noise ratio (SNR) value of the scanning electron microscope (SEM) images is proposed. This technique is known as autocorrelation Levinson-Durbin recursion (ACLDR) model. To test the performance of this technique, the SEM image is corrupted with noise. The autocorrelation function of the original image and the noisy image are formed. The signal spectrum based on the autocorrelation function of image is formed. ACLDR is then used as an SNR estimator to quantify the signal spectrum of noisy image. The SNR values of the original image and the quantified image are calculated. The ACLDR is then compared with the three existing techniques, which are nearest neighbourhood, first-order linear interpolation and nearest neighbourhood combined with first-order linear interpolation. It is shown that ACLDR model is able to achieve higher accuracy in SNR estimation.

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